scholarly journals Energy-Saving and Sustainable Separation of Bioalcohols by Adsorption on Bone Char

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Oslery Becerra-Pérez ◽  
Stavros Georgopoulos ◽  
Maria Lanara ◽  
Hilda Elizabeth Reynel-Ávila ◽  
Maria Papadaki ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adsorption Kinetics ◽  
Hydrophobic Interactions ◽  
Bone Char ◽  
Binary Adsorption ◽  
Industrial Chemicals ◽  
Adsorption Capacities ◽  
Fermentation Broths ◽  
Kinetics And Thermodynamic

The separation of ethanol, propanol, and butanol from aqueous solutions was studied using adsorption on bone char. Adsorption kinetics and thermodynamic parameters of this separation method were studied at different conditions of pH and temperature. Results showed that the maximum adsorption capacities of these bioalcohols were obtained at pH 6 and 20°C. An exothermic separation was identified, which can be mainly associated to hydrophobic interactions between bone char surface and bioalcohols. Binary adsorption studies were also performed using mixtures of these bioalcohols. An antagonistic adsorption was observed for all bioalcohols where the ethanol and propanol separation was significantly affected by butanol. A model based on an artificial neural network was proposed to correlate both single and binary adsorption isotherms of these bioalcohols with bone char. It was concluded that the bone char could be an interesting adsorbent for the sustainable separation and recovery of bioalcohols from fermentation broths, which are actually considered emerging liquid biofuels and relevant industrial chemicals.

Modelling Pb(II) adsorption based on synthetic and industrial wastewaters by ostrich bone char using artificial neural network and multivariate non-linear regression

2013 ◽  
Vol 3 (3) ◽  
pp. 221 ◽  
Author(s):  
Mohammad Javad Amiri ◽  
Jahangir Abedi Koupai ◽  
Saeid Eslamian ◽  
Sayed Farhad Mousavi ◽  
Mohammad Arshadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Bone Char ◽  
Industrial Wastewaters ◽  
Non Linear ◽  
Artificial Neural

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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